The method for configuring the network of a factory energy management system (FEMS) includes receiving a parameter set for configuration of the network, generating a diagram based on a characteristic for the parameter of each of communication schemes available for configuration of the network, determining, based on the diagram, a communication characteristic matrix including a characteristic value representing the characteristic for the parameter of each of the communication schemes, determining a correlation matrix representing a weight for the parameter in each process to be managed by the FEMS, deriving a communication adaptability matrix by applying the weight corresponding to the parameter for each process of the correlation matrix to the characteristic value for the parameter of each of the communication schemes included in the communication characteristic matrix, and determining a communication scheme to be applied for each process from among the communication schemes using the communication adaptability matrix.
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3. The method of claim 1, wherein the parameter is set based on an analysis on each process.
This invention relates to optimizing process parameters in a manufacturing or industrial system. The problem addressed is the inefficiency and variability in process performance due to suboptimal parameter settings, which can lead to defects, waste, or reduced throughput. The invention provides a method to dynamically adjust process parameters based on real-time analysis of each individual process step. The method involves monitoring process data from sensors or other measurement devices to assess performance metrics such as output quality, speed, or energy consumption. A computational model or algorithm analyzes this data to determine the optimal parameter settings for each process. These parameters could include temperature, pressure, flow rate, or machine speed, depending on the application. The system then adjusts the parameters in real time to improve efficiency, consistency, or other performance criteria. The invention may be applied in various industries, including semiconductor manufacturing, chemical processing, or automotive production, where precise control of process variables is critical. By continuously analyzing and adjusting parameters for each process, the method ensures that operations remain within desired tolerances, reducing defects and improving overall productivity. The system may also incorporate machine learning or predictive analytics to refine parameter adjustments over time based on historical data.
6. The method of claim 1, wherein at least one of a low operating expense (OPEX) characteristic, a low capital expense (CAPEX) characteristic, a flexibility characteristic, a high data rate characteristic, a high reliability characteristic, a low latency characteristic, a low power characteristic, and a low cost characteristic of the network is set as the parameter.
This invention relates to optimizing network performance by selecting and adjusting specific operational parameters to achieve desired characteristics. The technology addresses the challenge of balancing competing network requirements, such as cost, speed, reliability, and efficiency, in a way that meets user or system demands without unnecessary resource expenditure. The method involves configuring a network by setting at least one key parameter to prioritize specific performance attributes. These attributes include low operational expenses (OPEX), low capital expenses (CAPEX), flexibility, high data rates, high reliability, low latency, low power consumption, and low overall cost. By adjusting these parameters, the network can be tailored to different use cases, such as cost-sensitive deployments, high-performance applications, or energy-efficient operations. The approach ensures that the network operates within predefined constraints while maximizing the desired characteristic. For example, a low-OPEX setting may reduce ongoing maintenance costs, while a high-reliability setting may enhance fault tolerance. The method allows dynamic or static configuration based on the application requirements, ensuring optimal performance without compromising other critical aspects. This adaptability makes the solution suitable for various industries, including telecommunications, data centers, and IoT networks.
9. The apparatus of claim 7, wherein the parameter is set based on an analysis on each process.
This invention relates to an apparatus for optimizing process parameters in a manufacturing or industrial system. The problem addressed is the need to dynamically adjust process parameters to improve efficiency, quality, or performance while accounting for variations in individual processes. The apparatus includes a controller that monitors and analyzes each process to determine optimal parameter settings. The parameter adjustment is based on real-time or historical data analysis of each process, ensuring that the settings are tailored to the specific conditions of the process being controlled. This allows for fine-tuned adjustments that enhance overall system performance. The apparatus may also include sensors or feedback mechanisms to gather data on process conditions, which are then used to refine the parameter settings. By dynamically setting parameters based on process-specific analysis, the invention improves adaptability and reduces inefficiencies caused by static or generic parameter configurations. The system can be applied to various industrial processes, such as manufacturing, chemical processing, or automation, where precise control of parameters is critical. The invention ensures that each process operates at its optimal state, leading to better resource utilization, reduced waste, and improved output quality.
10. The apparatus of claim 7, wherein the instruction further comprises an operation of adding a symbol corresponding to each value included in the communication adaptability matrix to the communication adaptability matrix.
This invention relates to communication adaptability in networked systems, addressing the challenge of dynamically adjusting communication parameters to optimize performance in varying network conditions. The apparatus includes a processor and memory storing instructions for generating a communication adaptability matrix, which maps values representing adaptability metrics for different communication parameters. These metrics may include latency, bandwidth, reliability, or other performance indicators. The apparatus further includes instructions for adding symbols to the matrix, where each symbol corresponds to a specific value in the matrix, enhancing readability and interpretability of the adaptability data. The symbols may represent thresholds, ranges, or qualitative assessments (e.g., high, medium, low) for the communication parameters. This allows the system to quickly identify optimal or suboptimal communication configurations based on the symbolized matrix. The apparatus may also include instructions for updating the matrix in real-time as network conditions change, ensuring continuous adaptation. The symbolization step improves usability by abstracting raw numerical data into actionable insights, aiding in decision-making for network management or protocol selection. The invention is particularly useful in dynamic environments where communication parameters must be frequently adjusted to maintain efficiency and reliability.
12. The apparatus of claim 7, wherein at least one of a low OPEX characteristic, a low CAPEX characteristic, a flexibility characteristic, a high data rate characteristic, a high reliability characteristic, a low latency characteristic, a low power characteristic, and a low cost characteristic of the network is set as the parameter.
This invention relates to network optimization, specifically improving operational efficiency and performance in communication networks. The problem addressed is the need to balance various network attributes such as cost, reliability, and speed to meet specific operational requirements. Traditional networks often struggle to optimize multiple parameters simultaneously, leading to inefficiencies in deployment and operation. The apparatus includes a network optimization system that adjusts network parameters to achieve desired characteristics. These characteristics include low operational expenditure (OPEX), low capital expenditure (CAPEX), flexibility, high data rates, high reliability, low latency, low power consumption, and low cost. The system dynamically configures the network to prioritize one or more of these attributes based on predefined criteria or real-time conditions. For example, a network may be optimized for low latency in real-time applications or for low power consumption in energy-constrained environments. The apparatus ensures that the network operates efficiently while meeting the specified performance and cost constraints. This approach allows for tailored network configurations that adapt to different use cases, improving overall network performance and resource utilization.
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February 4, 2022
June 4, 2024
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